Research on the error probability distribution of photovoltaic output prediction based on output fluctuation characteristics and Generalized Gaussian Mixture Model

Peng Yan,Can Su,Chenmeng Xiang,Wen Zhou,P Siano,Q Li

doi:10.1051/e3sconf/202125602004

Abstract

Photovoltaic power output forecast error exists objectively and inevitably, and it can provide a guarantee for safe and stable operation of the power system through analyzing its characteristics. In this paper, the influence of predicted output fluctuation characteristics (predicted output amplitude and power variation) on prediction error was studied based on the analysis of variance (ANOVA) method. The prediction error conditions were classified into six types based on the clustering of numerical characteristics of predicted output. Then, a Generalized Gaussian Mixture Model (GGMM) was proposed to fit the prediction error distribution of each type of photovoltaic output. The mean absolute error (MAE), coefficient of determination (R2), and root mean square error (RMSE) were used as accuracy evaluation indexes. The example analysis showed that the GGMM can satisfy the asymmetry and kurtosis diversity of the error distribution after division by conditions, and the fitting result is better than that of the normal distribution, improved Laplace distribution and t Location-Scale distribution model.

Highlights

At present, there are only a few studies on the prediction errors of photovoltaic power generation in the literature
A generalized Gaussian mixture model is proposed to describe the distribution of photovoltaic power output prediction errors
In the case of the kurtosis value slightly higher than meet the characteristics of the high and lean distribution the normal distribution, the normal distribution has a of the photovoltaic output prediction error

Summary

Introduction

There are only a few studies on the prediction errors of photovoltaic power generation in the literature. A generalized Gaussian mixture model is proposed to describe the distribution of photovoltaic power output prediction errors.

Results

Conclusion